# VideoMemory: Toward Consistent Video Generation via Memory Integration

arXiv: https://arxiv.org/abs/2601.03655 · HTML: https://mlsanity.com/paper/2601.03655/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 37 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 30 of 31)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion](https://mlsanity.com/paper/2506.08009/) (2025)
- [MAGI-1: Autoregressive Video Generation at Scale](https://mlsanity.com/paper/2505.13211/) (2025)
- [LongLive: Real-time Interactive Long Video Generation](https://mlsanity.com/paper/2509.22622/) (2025)
- [StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation](https://mlsanity.com/paper/2405.01434/) (2024)
- [Rolling Forcing: Autoregressive Long Video Diffusion in Real Time](https://mlsanity.com/paper/2509.25161/) (2025)
- [SkyReels-V2: Infinite-length Film Generative Model](https://mlsanity.com/paper/2504.13074/) (2025)
- [Self-Forcing++: Towards Minute-Scale High-Quality Video Generation](https://mlsanity.com/paper/2510.02283/) (2025)
- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://mlsanity.com/paper/2312.04433/) (2023)
- [Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval](https://mlsanity.com/paper/2506.03141/) (2025)
- [In-Context LoRA for Diffusion Transformers](https://mlsanity.com/paper/2410.23775/) (2024)
- [Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models](https://mlsanity.com/paper/2504.12626/) (2025)
- [VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning](https://mlsanity.com/paper/2309.15091/) (2023)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://mlsanity.com/paper/1812.02784/) (2018)
- [Video World Models with Long-term Spatial Memory](https://mlsanity.com/paper/2506.05284/) (2025)
- [Long Context Tuning for Video Generation](https://mlsanity.com/paper/2503.10589/) (2025)
- [One-Minute Video Generation with Test-Time Training](https://mlsanity.com/paper/2504.05298/) (2025)
- [Automated Movie Generation via Multi-Agent CoT Planning](https://mlsanity.com/paper/2503.07314/) (2025)
- [RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers](https://mlsanity.com/paper/2502.15894/) (2025)
- [VideoStudio: Generating Consistent-Content and Multi-Scene Videos](https://mlsanity.com/paper/2401.01256/) (2024)
- [Magic-Me: Identity-Specific Video Customized Diffusion](https://mlsanity.com/paper/2402.09368/) (2024)
- [HoloCine: Holistic Generation of Cinematic Multi-Shot Long Video Narratives](https://mlsanity.com/paper/2510.20822/) (2025)
- [ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models](https://mlsanity.com/paper/2505.07652/) (2025)
- [EchoShot: Multi-Shot Portrait Video Generation](https://mlsanity.com/paper/2506.15838/) (2025)
- [FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation](https://mlsanity.com/paper/2506.18899/) (2025)
- [ROICtrl: Boosting Instance Control for Visual Generation](https://mlsanity.com/paper/2411.17949/) (2024)

## Built on by (most cited first, 25 of 25)

- [Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion](https://mlsanity.com/paper/2602.07775/) (2026)
- [ViMax: Agentic Video Generation](https://mlsanity.com/paper/2606.07649/) (2026)
- [WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction](https://mlsanity.com/paper/2605.29341/) (2026)
- [A2RD: Agentic Autoregressive Diffusion for Long Video Consistency](https://mlsanity.com/paper/2605.06924/) (2026)
- [EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation](https://mlsanity.com/paper/2605.15199/) (2026)
- [ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation](https://mlsanity.com/paper/2605.26525/) (2026)
- [Echo-Memory: A Controlled Study of Memory in Action World Models](https://mlsanity.com/paper/2606.09803/) (2026)
- [UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating](https://mlsanity.com/paper/2606.21661/) (2026)
- [Head Forcing: Long Autoregressive Video Generation via Head Heterogeneity](https://mlsanity.com/paper/2605.14487/) (2026)
- [GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling](https://mlsanity.com/paper/2606.20799/) (2026)
- [DIM-WAM: World-Action Modeling with Diverse Historical Event Memory](https://mlsanity.com/paper/2606.27677/) (2026)
- [CANVAS: Continuity-Aware Narratives via Visual Agentic Storyboarding](https://mlsanity.com/paper/2604.13452/) (2026)
- [RECAP-Forcing: Retaining Content Appearances for Long Video Generation](https://mlsanity.com/paper/2608.26671/) (2026)
- [SlotMem: Character-Addressable Internal Memory for Narrative Long Video Generation](https://mlsanity.com/paper/2607.15772/) (2026)
- [FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling](https://mlsanity.com/paper/2607.19038/) (2026)
- [KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation](https://mlsanity.com/paper/2607.14202/) (2026)
- [Vorch-Director: Interactive World Story Model via Noise-Aware Error Rectification](https://mlsanity.com/paper/2608.05776/) (2026)
- [SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale](https://mlsanity.com/paper/2608.22725/) (2026)
- [KathaTrace: Diagnosing Semantic Trajectory Collapse in Generated Visual Narratives](https://mlsanity.com/paper/2607.01312/) (2026)
- [FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production](https://mlsanity.com/paper/2608.29814/) (2026)
- [DramaChain Bench: An End-to-End Benchmark for Short-Drama Generation](https://mlsanity.com/paper/2609.00646/) (2026)
- [Complementary Retrieval-Augmented Prompting for Consistent Long-Form Video Generation](https://mlsanity.com/paper/2609.37407/) (2026)
- [DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency](https://mlsanity.com/paper/2609.39096/) (2026)
- [Memory-Guided B-Roll Generation from User Video Collections](https://mlsanity.com/paper/2610.01884/) (2026)
- [Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation](https://mlsanity.com/paper/2610.20571/) (2026)

## Abstract (arXiv)

Maintaining consistent characters, props, and environments across multiple shots is a central challenge in narrative video generation. Existing models can produce high-quality short clips but often fail to preserve entity identity and appearance when scenes change or when entities reappear after long temporal gaps. We present VideoMemory, an entity-centric framework that integrates narrative planning with visual generation through a Dynamic Memory Bank. Given a structured script, a multi-agent system decomposes the narrative into shots, retrieves entity representations from memory, and synthesizes keyframes and videos conditioned on these retrieved states. The Dynamic Memory Bank stores explicit visual and semantic descriptors for characters, props, and backgrounds, and is updated after each shot to reflect story-driven changes while preserving identity. This retrieval-update mechanism enables consistent portrayal of entities across distant shots and supports coherent long-form generation. To evaluate this setting, we construct a 54-case multi-shot consistency benchmark covering character-, prop-, and background-persistent scenarios. Extensive experiments show that VideoMemory achieves strong entity-level coherence and high perceptual quality across diverse narrative sequences.